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How To Forecast And Analyze Your Retail Financial Models

When it comes to financial forecasting and analysis, the retail world sees its fair share on a regular basis. Perhaps more so than ever, the need for these processes is increasing week by week due to today’s rapidly developing online retail market. Companies rush to quickly adapt to constant fluctuations and uncertainties and financial forecasting allows them to know when to take more risks, and when to tighten the wallet. Having a deeper understanding of financial planning increases chances of avoiding bumps on the way.

When it comes down to brass tacks, all financial forecasts are informed guesses as nobody can be a 100% certain about future development. Hence, it helps to have as accurate data as possible, while also being proficient at the following analysis. This post will show how to forecast and analyze your retail financial models so you can get a better picture about your online operation – both as a whole and by category.

Data is essential

It’s no secret that a retailer focuses his financial model on profit for the most part. In order to achieve the desired levels of profit, a retail business sets a forecast as a guide. For that, it needs data and lots of it. This especially matters with larger brands like Tesco, for example, who have huge volumes of data to sift through and shift to retail analytics to retain their market share in the face of overwhelming competition.

There are usually two ways or types of data acquired:

Current market data (qualitative approach) – descriptive data that describes the characteristics and properties of the product instead of measuring them (traffic, product or market fit, product features and so on).

The difference here is directly applicable to accuracy and duration of the forecast (short-term or long-term). Generally, these forecasts are updated on a monthly or quarterly basis and provide global and regional projections. Both short-term and long-term forecasts and analyses can help retailers understand the driving forces behind their sales, albeit through different ways.

By using current market data, a business seeks to set a short-term forecast based on the opinion of experts. For instance, a new product or service is being launched and the company wants to determine the short-term success of sales. This is achieved through broad market research that includes polling people on specific aspects of the product, compiling opinions of field experts and historical life-cycle analogy. As a result, the forecast is rather subjective as it is based on the opinion and judgment of different groups such as consumers and experts. However, it is an adequate choice when there is no past data to appropriate.

On the other hand, relying on past numerical data expands the scope of the forecast. Crunching the numbers by looking back at available past data can uncover patterns that are expected to continue in the future. That means predicting variables like prices, sales, demand and others in the long run. Essentially, this is a more math-based approach that relies on consistency of data over certain periods and thus, more accurate. Math is an exact science, whether you like it or not.

The process

As an example, we’ll take revenue growth, one of the most basic and simple approaches to forecasting the revenue of a company, which, in itself, is one of the main retail financial models. Revenue growth for specifically set forecast periods is built upon historical trends and future guidance (either qualitative or quantitative). So, the first step is choosing a data point on which the forecast will be based.

As seen in the image above, that is the span of five years (2009 – 2013). A calculation of year-over-year (y-o-y) growth rates is performed and based on the historical trend, assumptions for the revenue growth are made for the forecasted period (2014 – 2016). Then, we calculate revenues for the forecasted periods based on the assumed revenue growth and as a result, you get a fairly good estimate of how your business will perform year after year.

Notice that although we mentioned earlier that math is an exact science (except for Pi, it kinda goes on and on), the forecast requires a certain level of assumptions in order to work. This can be further minimized by utilizing modern technology techniques that closely follows both current and past trends to give the best possible projection. This element is also closely tied to analysis, which we’ll focus on next.

The analysis part

This part is more important because it brings the decision-level insights that can shape the future of a business. In our example, it comes down to the nitty-gritty of the whole revenue model – what will be the key drivers, what periods will provide better sales, and so on.

For the most part, the analysis is still done the old-fashioned way – through Excel (as evidenced above). It’s a great tool that turns worksheets into detailed financial models. The trouble is, you need to have good or advanced Excel skills so you might want to invest in an Excel course. So, apart from the “advanced Excel skills” requirement for the retail financial analyst position today, working in Excel also takes time as not only do you have to create a complex model, but also to effective summarize it – highlight all the risks, rewards, and critical factors audiences that might not be on your level of understanding.

Another problem is that the basis for the forecast is historical data which is old with no guarantee things will translate well into the future. In addition, it is hard to factor in unexpected or unique events once the ball gets rolling as that demands a do-over or much of it.

Alternatively, a company looking to have the most accurate retail forecast and analysis has the option of utilizing analytics software to hastily grasp how its products and services are performing on the market. It brings a simplified overview of the entire forecast and analysis process that helps with strategic oversights and decisions. With an analytics software, you get the optimal breadth and depth of key metrics and, most importantly, you get them in real-time. That means that data is always updated and consistent so you can apply risk and uncertainty predictions more accurately.

For instance, tracking your competition’s fast-selling products allows you to quickly understand your product positioning and gap analysis, if any. Following shifting market trends with an encompassing retail analytics software grants you real-time overview of market movements and by having insights into your competition’s’ pricing, promotion and catalog movement, you are able to plan ahead your future with ease. The key is marrying both qualitative and quantitative approach into one complete package for maximum benefits.

Conclusion

The retail business is one the most diversified sectors that covers a massive amount of different products. Smart business decision-making demands that you know your numbers. In that way, financial models help out with projecting a company’s financial performance. When a retail company performs its financial forecast and analysis, it seeks to validate its specific goals and priorities, as well as identify with high levels of accuracy what is needed to achieve them. Used properly, it can keep your business ahead of your competition and drive profits, as well as improve your business.